{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 车贷违约预测分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>客户编号</th>\n",
       "      <th>已发货款</th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比列</th>\n",
       "      <th>品牌</th>\n",
       "      <th>骑车销售商</th>\n",
       "      <th>车厂</th>\n",
       "      <th>出生日期</th>\n",
       "      <th>货款日期</th>\n",
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       "      <th>...</th>\n",
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       "      <th>已发放贷款总额</th>\n",
       "      <th>每月还款总额</th>\n",
       "      <th>贷款与已还贷款比列</th>\n",
       "      <th>主账户还款期数</th>\n",
       "      <th>次账户还款期数</th>\n",
       "      <th>贷款与已批准贷款比列</th>\n",
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       "      <td>89.55</td>\n",
       "      <td>61</td>\n",
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       "      <th>2</th>\n",
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       "      <td>5</td>\n",
       "      <td>15663</td>\n",
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       "      <td>1977</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>648134</td>\n",
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       "      <td>99750</td>\n",
       "      <td>73.68</td>\n",
       "      <td>76</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>65450</td>\n",
       "      <td>79.45</td>\n",
       "      <td>146</td>\n",
       "      <td>14181</td>\n",
       "      <td>45</td>\n",
       "      <td>1974</td>\n",
       "      <td>2018</td>\n",
       "      <td>17</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "     客户编号   已发货款   资产成本  贷款与资产比列   品牌  骑车销售商  车厂  出生日期  货款日期  地区  ...  \\\n",
       "0  601758  65532  78990    84.38  136  20490  45  1981  2018   8  ...   \n",
       "1  519488  56759  65325    89.55   61  22778  86  1967  2018   6  ...   \n",
       "2  447579  58413  67960    89.02    5  15663  86  1977  2018   9  ...   \n",
       "3  648134  72317  99750    73.68   76  17242  48  1995  2018   8  ...   \n",
       "4  458210  50078  65450    79.45  146  14181  45  1974  2018  17  ...   \n",
       "\n",
       "   尚未还清有效贷款总额  已批准贷款总额  已发放贷款总额  每月还款总额  贷款与已还贷款比列  主账户还款期数  次账户还款期数  \\\n",
       "0           0        0        0       0       1.00        0        0   \n",
       "1     2054139  2036500  2036500   34455       0.99       59        0   \n",
       "2           0        0        0       0       1.00        0        0   \n",
       "3           0    13813    13813       0   13814.00    13813        0   \n",
       "4      467161   550000   550000   12863       1.18       42        0   \n",
       "\n",
       "   贷款与已批准贷款比列  总贷款次数与总有效贷款次数比  工作类型  \n",
       "0         1.0            1.00     0  \n",
       "1         1.0            1.33     1  \n",
       "2         1.0            1.00     1  \n",
       "3         1.0            2.00     0  \n",
       "4         1.0            1.06     1  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info = pd.read_csv(r\"C:/Users/lsw/Desktop/numpy/算法_01/车贷违约预测_01.csv\")\n",
    "user_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 观察数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>83.6</td>\n",
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       "      <th>品牌</th>\n",
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       "      <td>61.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>2.610000e+02</td>\n",
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       "      <td>2.480300e+04</td>\n",
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       "      <th>车厂</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>69.1</td>\n",
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       "      <td>45.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.560000e+02</td>\n",
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       "    <tr>\n",
       "      <th>出生日期</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1983.9</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>1986.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>1449.0</td>\n",
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       "      <th>是否填写手机号</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>信用评分</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>291.8</td>\n",
       "      <td>339.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>8.900000e+02</td>\n",
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       "    <tr>\n",
       "      <th>主账户贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.530000e+02</td>\n",
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       "      <th>主账户有效贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>主账户中尚未还清有效贷款</th>\n",
       "      <td>199717.0</td>\n",
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       "      <td>963804.3</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35899.0</td>\n",
       "      <td>9.652492e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中已批准的贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>222432.3</td>\n",
       "      <td>2522528.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中已发放贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>222042.0</td>\n",
       "      <td>2525814.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>次账户有效贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户中尚未还清有效贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>5583.9</td>\n",
       "      <td>168672.8</td>\n",
       "      <td>-574647.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.603285e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户中已批准贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>7491.0</td>\n",
       "      <td>181836.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.688820e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户中已发放贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>7374.5</td>\n",
       "      <td>181233.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.688820e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户每月还款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>13144.2</td>\n",
       "      <td>152428.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2.564281e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户没用还款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>301.4</td>\n",
       "      <td>13045.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.246710e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>近六个月新贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>近六个月违约次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均贷款期限</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.170000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.170000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款查询次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否违约</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与资产比</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>9.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款总次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.530000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户无效贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.510000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户无效贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>无效贷款总次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.510000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>174312.5</td>\n",
       "      <td>981364.0</td>\n",
       "      <td>-6678296.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38189.0</td>\n",
       "      <td>9.652492e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已批准贷款总额</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>229923.3</td>\n",
       "      <td>2530977.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67206.0</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已发放贷款总额</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>229416.5</td>\n",
       "      <td>2534184.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65085.0</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>每月还款总额</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>13445.5</td>\n",
       "      <td>153161.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2094.0</td>\n",
       "      <td>2.564281e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与已还贷款比列</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-110000.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户还款期数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>50595.8</td>\n",
       "      <td>2275670.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户还款期数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>2928.0</td>\n",
       "      <td>106541.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.980000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与已批准贷款比列</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>553.6</td>\n",
       "      <td>114134.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工作类型</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      mean        std        min       25%       50%  \\\n",
       "客户编号            199717.0  535690.9    68193.4   417428.0  476762.0  535571.0   \n",
       "已发货款            199717.0   54256.3    12977.7    13320.0   46977.0   53703.0   \n",
       "资产成本            199717.0   75823.9    18928.9    37000.0   65714.0   70922.0   \n",
       "贷款与资产比列         199717.0      74.6       11.5       10.0      68.7      76.7   \n",
       "品牌              199717.0      72.7       69.7        1.0      14.0      61.0   \n",
       "骑车销售商           199717.0   19634.0     3493.7    10524.0   16505.0   20333.0   \n",
       "车厂              199717.0      69.1       22.1       45.0      48.0      86.0   \n",
       "出生日期            199717.0    1983.9        9.8     1949.0    1977.0    1986.0   \n",
       "货款日期            199717.0    2018.0        0.0     2018.0    2018.0    2018.0   \n",
       "地区              199717.0       7.2        4.5        1.0       4.0       6.0   \n",
       "对接员工编号          199717.0    1547.9      974.9        1.0     712.0    1449.0   \n",
       "是否填写手机号         199717.0       1.0        0.0        1.0       1.0       1.0   \n",
       "受否填写身份证         199717.0       1.0        0.0        1.0       1.0       1.0   \n",
       "是否出具驾驶证         199717.0       0.0        0.2        0.0       0.0       0.0   \n",
       "是否填写护照          199717.0       0.0        0.0        0.0       0.0       0.0   \n",
       "信用评分            199717.0     291.8      339.3        0.0       0.0      14.0   \n",
       "主账户贷款次数         199717.0       2.5        5.3        0.0       0.0       1.0   \n",
       "主账户有效贷款次数       199717.0       1.0        2.0        0.0       0.0       0.0   \n",
       "主账户中尚未还清有效贷款    199717.0  168728.6   963804.3 -6678296.0       0.0       0.0   \n",
       "主账户中已批准的贷款      199717.0  222432.3  2522528.2        0.0       0.0       0.0   \n",
       "主账户中已发放贷款       199717.0  222042.0  2525814.3        0.0       0.0       0.0   \n",
       "次账户贷款次数         199717.0       0.1        0.6        0.0       0.0       0.0   \n",
       "次账户有效贷款次数       199717.0       0.0        0.3        0.0       0.0       0.0   \n",
       "次账户中尚未还清有效贷款    199717.0    5583.9   168672.8  -574647.0       0.0       0.0   \n",
       "次账户中已批准贷款       199717.0    7491.0   181836.2        0.0       0.0       0.0   \n",
       "次账户中已发放贷款       199717.0    7374.5   181233.2        0.0       0.0       0.0   \n",
       "主账户每月还款         199717.0   13144.2   152428.9        0.0       0.0       0.0   \n",
       "次账户没用还款         199717.0     301.4    13045.3        0.0       0.0       0.0   \n",
       "近六个月新贷款次数       199717.0       0.4        1.0        0.0       0.0       0.0   \n",
       "近六个月违约次数        199717.0       0.1        0.4        0.0       0.0       0.0   \n",
       "平均贷款期限          199717.0       8.1       13.9        0.0       0.0       0.0   \n",
       "第一次贷款距今时间       199717.0      13.2       21.2        0.0       0.0       0.0   \n",
       "贷款查询次数          199717.0       0.2        0.7        0.0       0.0       0.0   \n",
       "是否违约            199717.0       0.2        0.4        0.0       0.0       0.0   \n",
       "贷款与资产比          199717.0       0.7        0.1        0.1       0.7       0.7   \n",
       "贷款总次数           199717.0       2.5        5.4        0.0       0.0       1.0   \n",
       "主账户无效贷款次数       199717.0       1.4        4.0        0.0       0.0       0.0   \n",
       "次账户无效贷款次数       199717.0       0.0        0.4        0.0       0.0       0.0   \n",
       "无效贷款总次数         199717.0       1.4        4.1        0.0       0.0       0.0   \n",
       "尚未还清有效贷款总额      199717.0  174312.5   981364.0 -6678296.0       0.0       0.0   \n",
       "已批准贷款总额         199717.0  229923.3  2530977.1        0.0       0.0       0.0   \n",
       "已发放贷款总额         199717.0  229416.5  2534184.8        0.0       0.0       0.0   \n",
       "每月还款总额          199717.0   13445.5   153161.8        0.0       0.0       0.0   \n",
       "贷款与已还贷款比列       199717.0       inf        NaN  -110000.3       1.0       1.0   \n",
       "主账户还款期数         199717.0   50595.8  2275670.5        0.0       0.0       0.0   \n",
       "次账户还款期数         199717.0    2928.0   106541.0        0.0       0.0       0.0   \n",
       "贷款与已批准贷款比列      199717.0     553.6   114134.3        0.0       1.0       1.0   \n",
       "总贷款次数与总有效贷款次数比  199717.0       1.4        0.8        1.0       1.0       1.0   \n",
       "工作类型            199717.0       0.5        0.6        0.0       0.0       0.0   \n",
       "\n",
       "                     75%           max  \n",
       "客户编号            594571.0  6.710840e+05  \n",
       "已发货款             60247.0  9.905720e+05  \n",
       "资产成本             79159.0  1.628992e+06  \n",
       "贷款与资产比列             83.6  9.500000e+01  \n",
       "品牌                 130.0  2.610000e+02  \n",
       "骑车销售商            23000.0  2.480300e+04  \n",
       "车厂                  86.0  1.560000e+02  \n",
       "出生日期              1992.0  2.000000e+03  \n",
       "货款日期              2018.0  2.018000e+03  \n",
       "地区                  10.0  2.200000e+01  \n",
       "对接员工编号            2357.0  3.795000e+03  \n",
       "是否填写手机号              1.0  1.000000e+00  \n",
       "受否填写身份证              1.0  1.000000e+00  \n",
       "是否出具驾驶证              0.0  1.000000e+00  \n",
       "是否填写护照               0.0  1.000000e+00  \n",
       "信用评分               680.0  8.900000e+02  \n",
       "主账户贷款次数              3.0  4.530000e+02  \n",
       "主账户有效贷款次数            1.0  1.440000e+02  \n",
       "主账户中尚未还清有效贷款     35899.0  9.652492e+07  \n",
       "主账户中已批准的贷款       64000.0  1.000000e+09  \n",
       "主账户中已发放贷款        62000.0  1.000000e+09  \n",
       "次账户贷款次数              0.0  5.200000e+01  \n",
       "次账户有效贷款次数            0.0  3.600000e+01  \n",
       "次账户中尚未还清有效贷款         0.0  3.603285e+07  \n",
       "次账户中已批准贷款            0.0  2.688820e+07  \n",
       "次账户中已发放贷款            0.0  2.688820e+07  \n",
       "主账户每月还款           2000.0  2.564281e+07  \n",
       "次账户没用还款              0.0  3.246710e+06  \n",
       "近六个月新贷款次数            0.0  3.500000e+01  \n",
       "近六个月违约次数             0.0  2.000000e+01  \n",
       "平均贷款期限              13.0  1.170000e+02  \n",
       "第一次贷款距今时间           20.0  1.170000e+02  \n",
       "贷款查询次数               0.0  2.800000e+01  \n",
       "是否违约                 0.0  1.000000e+00  \n",
       "贷款与资产比               0.8  9.000000e-01  \n",
       "贷款总次数                3.0  4.530000e+02  \n",
       "主账户无效贷款次数            1.0  4.510000e+02  \n",
       "次账户无效贷款次数            0.0  4.200000e+01  \n",
       "无效贷款总次数              1.0  4.510000e+02  \n",
       "尚未还清有效贷款总额       38189.0  9.652492e+07  \n",
       "已批准贷款总额          67206.0  1.000000e+09  \n",
       "已发放贷款总额          65085.0  1.000000e+09  \n",
       "每月还款总额            2094.0  2.564281e+07  \n",
       "贷款与已还贷款比列            1.3           inf  \n",
       "主账户还款期数             25.0  1.000000e+09  \n",
       "次账户还款期数              0.0  1.980000e+07  \n",
       "贷款与已批准贷款比列           1.0  5.000000e+07  \n",
       "总贷款次数与总有效贷款次数比       1.7  1.800000e+01  \n",
       "工作类型                 1.0  2.000000e+00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info.describe().T.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199717 entries, 0 to 199716\n",
      "Data columns (total 49 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   客户编号            199717 non-null  int64  \n",
      " 1   已发货款            199717 non-null  int64  \n",
      " 2   资产成本            199717 non-null  int64  \n",
      " 3   贷款与资产比列         199717 non-null  float64\n",
      " 4   品牌              199717 non-null  int64  \n",
      " 5   骑车销售商           199717 non-null  int64  \n",
      " 6   车厂              199717 non-null  int64  \n",
      " 7   出生日期            199717 non-null  int64  \n",
      " 8   货款日期            199717 non-null  int64  \n",
      " 9   地区              199717 non-null  int64  \n",
      " 10  对接员工编号          199717 non-null  int64  \n",
      " 11  是否填写手机号         199717 non-null  int64  \n",
      " 12  受否填写身份证         199717 non-null  int64  \n",
      " 13  是否出具驾驶证         199717 non-null  int64  \n",
      " 14  是否填写护照          199717 non-null  int64  \n",
      " 15  信用评分            199717 non-null  int64  \n",
      " 16  主账户贷款次数         199717 non-null  int64  \n",
      " 17  主账户有效贷款次数       199717 non-null  int64  \n",
      " 18  主账户中尚未还清有效贷款    199717 non-null  int64  \n",
      " 19  主账户中已批准的贷款      199717 non-null  int64  \n",
      " 20  主账户中已发放贷款       199717 non-null  int64  \n",
      " 21  次账户贷款次数         199717 non-null  int64  \n",
      " 22  次账户有效贷款次数       199717 non-null  int64  \n",
      " 23  次账户中尚未还清有效贷款    199717 non-null  int64  \n",
      " 24  次账户中已批准贷款       199717 non-null  int64  \n",
      " 25  次账户中已发放贷款       199717 non-null  int64  \n",
      " 26  主账户每月还款         199717 non-null  int64  \n",
      " 27  次账户没用还款         199717 non-null  int64  \n",
      " 28  近六个月新贷款次数       199717 non-null  int64  \n",
      " 29  近六个月违约次数        199717 non-null  int64  \n",
      " 30  平均贷款期限          199717 non-null  int64  \n",
      " 31  第一次贷款距今时间       199717 non-null  int64  \n",
      " 32  贷款查询次数          199717 non-null  int64  \n",
      " 33  是否违约            199717 non-null  int64  \n",
      " 34  贷款与资产比          199717 non-null  float64\n",
      " 35  贷款总次数           199717 non-null  int64  \n",
      " 36  主账户无效贷款次数       199717 non-null  int64  \n",
      " 37  次账户无效贷款次数       199717 non-null  int64  \n",
      " 38  无效贷款总次数         199717 non-null  int64  \n",
      " 39  尚未还清有效贷款总额      199717 non-null  int64  \n",
      " 40  已批准贷款总额         199717 non-null  int64  \n",
      " 41  已发放贷款总额         199717 non-null  int64  \n",
      " 42  每月还款总额          199717 non-null  int64  \n",
      " 43  贷款与已还贷款比列       199717 non-null  float64\n",
      " 44  主账户还款期数         199717 non-null  int64  \n",
      " 45  次账户还款期数         199717 non-null  int64  \n",
      " 46  贷款与已批准贷款比列      199717 non-null  float64\n",
      " 47  总贷款次数与总有效贷款次数比  199717 non-null  float64\n",
      " 48  工作类型            199717 non-null  int64  \n",
      "dtypes: float64(5), int64(44)\n",
      "memory usage: 74.7 MB\n"
     ]
    }
   ],
   "source": [
    "user_info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1498760>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1bce850>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1d02790>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1e744f0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1ec7610>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1f9ae50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a20d24f0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a21c36a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a22a7790>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N6X55xSzwF0k+AdzD4MFDn2Rw8fGiJOczONJ+EvghMJPkLQx+3m8APp7k6qr6327YFx7nHPcHus+8jMFvgNnVjfExBhdBX5/kXAYP5//HfjqWjuWzStSULig/DXwduK2qvrpg2ysYPKf5XQye6Hc78G4Gv4rt88AbqupQkuuAH1XVbd1+36yqn170OTuB71fVrUnWd+9/tNv2IuBVVfWX3W9VeT+DR43+T6/NSx2DW5Ia433cktQYg1uSGmNwS1JjDG5JaozBLUmN+X/uZo5LP+cj9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a23889d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a2447e50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a20d2ee0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fAOxNcl1/3IVJAH4aeFTt/3yN1UmOouv1frxfN+yZ2pfRPar2vgPrHg78JPD7dD3jlybZ13se7HFD1+u+Ism7gB+nezTou4FH0j0Odz9V9WHgw0lOAF5TVecs3Ee6twxuTV1V3Q7clOSlwPPpeqrPBh5NN6zwxqra3t+pcW1VzQ4en+RDdM9epv/Cg1+lu+vjEXRBu++LH+4HnJ1kD/Aeuudx/PfAqVbRPYtlF/24d1W9H3j/wHvNMtDjHlj/YuBv6B7Q9OYk53LP3v2gDcDx/cON9nk03Rd0DBsTlw7I4NaySPJUurtKttENiby1qv50wW53AycnWfic6IcNLK8FXl9Vn+pv43sjsK2/IPkXwHVV9Y5+38cuqOEw4Hjgj+juQBnmfgx/OuLrq6qSXNCf5/nMX1hNkvXAz+1776r6GPCxgfc+AthpaGsxnICjqUvyELqLkmcDv0J3y95TkvxLkuuSfKe/Pe9w4DNVdergD13IrgKoqmv70D4MeAfwjqraN1TyZ8BTR3wN2avphjjeNaTOpwF/TXcb4X5qfgLEkcDTgU8NPJjqW3RPlDx6yDkfnORLdLcrvvUgdUkH5AQcLYskx1XVd0fscxhw/xrzm1yWU5LUmP+YkqyqqrsmXZMOXQa3JDXGoRJJaozBLUmNMbglqTEGtyQ1xuCWpMb8H1qSo7f9UgL5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1e58130>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1c6dd60>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1e3b400>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1e7d220>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dR019S1pfR0TkVAr9gLvz5KsNLJ9dxqT8nLS+1rVzysmJx/h3TfGIyBjT0TuBnYePsbupg0+smJX21yrIzeLq2aX8bvthvnzbAsz0bVoi6TLUUUJRPUJII/3AU682AHDzZZPH5PVuWTiFXY0dbDvQPiavJyICCv0TntzWwJVVk5g8RsfOv3vRNHLiMR7ZtG9MXk9EBDS9A8Delk62HWjny7fNH7PXLM7P5sbLKnn85QN8+bYFZMf1/iuZqbOnj5q6VmrqWnh5XxsHjhynoa2Lnr4kOVkxCnOzmDZpAheXTuDSyRPTvk8t6hT6wKOb9wNwy+VTx/R171gynd+80sBzbzRy4xhNK4mMha7eBM/taORXWw7w1LYGehOOAVOK8ygtyGHR9GLiZnT3JWg/3sf2hnY27WnFgJnlBWTFjdsXTxv161+JQp/eRJKfvlDPO+ZVUFWWP6av/c5LKygryOEXm/cp9AfQjrcLU28iybraJn615SCrtzVwtLuP0oIcllSVcPnUIqpK88kdIsTdnaZjPbyy/wgv723jC/+2lX/47XY+fHUVH1k+g8qJumTJaIl86D+1rYFD7d38/R0zxvy1s+Mxbn/bNH66YQ9tnb0U54/Ot3SJjJXjPQnW1TbxzOuHeGpbA62dvUzMy+KWhVN4z+JpXHNJGQ/XnH2/lZlRMTGXG+ZP5vpLK5lVUcAP1u7mW/9ey3efe5P3LJ7GJ66dxcKLisegV5kt8qH/o+frqCrN553z0ntC1lA+cOV0HlxXx09frOcvV84ZlzaIhOXuHGjrYu3ORp5+7TBraxvp6k1SmJvFDfMruX3xNK6bV05u1sinZcyMuqZObpg/mSumT+L5N5t5YstBfrFpP1fPKuVj18zk+vmVmvoZoUiH/rYDbbxU18q9ty0gnqZr7ZzNwouKWXlpBff/YRd3L5vBxDyN9uX80dnTx+sH29lUf4RNe1pZV9tEe1cfAJMmZPO2i0tYMHUiX7p1ATlZo38wQnlhLrcvnsZNCyZTU9/C1n1tfPqnmyjMzeJdCyq5bm4Fb59ZQlVpvs53CSnSof/gujrysmP8afXF49qOv75pHrd/ex0Prqvjs++aO65tkWjq6k1Qe/gYOw8f5dFNBzh8tItD7V20dvaeqCnJz2ZmeQFVpfnMKi9gSlHeiaBNR+APNCEnznVzK/jWh5bw/JvN/OaVg6x+7RC/fPnAibbNqSwMbhOZU1nI3MpCphZrX8CpIhv6W/Ye4ZFN+/j4NbPGfS79iumTuOmyyXxvzS7+bPnMcW+PZK6+RJLaxmO80XCUnYeOsePQUXYePkZ9cwdJT9XEzSifmMP0knyumpHLlKI8Li7NP+On0DNdG380ZcVjvGNeBe+YV8Hfvd+pbTzGi7tbeO1gO7WHjvHkqw20du49UV+QE6esMJdZ5QVcUlHIzPJ8smLRPjw6kqHfl0jy5UdfoaIwl8/ddH6MrD934zxu++Ya/vkPb/Lfbhm78wUks/1g7W72tHRS39xJfUsH+1qO05NIXd01ZlBWkEtlUS4rL61kclEelRNzKS/MHbfpzuGIxYx5kycyb/LEt6xvPtYdfGo5Ru3hY/x++2HW7GzkuR2NTMiOs/CiYq4a5e+/vpBEMvR/vL6ebQfa+ae7rjxv5tAvm1bEB66czr889yYr5pSn7Zu7JHO5O3tbjlNT30JNfSub6lt5o+EoDhgwdVIeV80o4eLSfKYU5VFemENWBp4UWFaYS1lhLlfPLgNg3uSJdPcl2NXYwdZ9R3h5bysv1bXw4u5mPnXdbG6+fMoF8SY3WiIX+m80HOVrq99g5aUV3LZoyng35y2+8t7L2bLvCJ/92Wae+OwKphZPGO8mjYtE0kkknZiRkaE0Wnr6krx6oI2Nda089vJ+6ps7Odad2smamxWjqjSfdy2opKq0gItLJ5zTETUXutysOAumFrFgahHdfQk21beyJdgpXFWazydXzOJPqqeTn5P5kWjuPt5tGFJ1dbXX1NSM2vZ2HDrKh+7fQFbceOTT1zC9ZHROxhrNk4lqDx/jvd9ey7wpE/nRJ5ZSdJ58EkmHju4+Nu85Qk19C6/ub6e+uYM9LZ10D/hymdzgNP3ywlxWzq/g8mnFLJ5eHLmjNZJJZ1dTB6/sP8LWfW28sq+NV/a3nfhZleRnM6OsgBll+VSV5jO5KI9YhH4+Z/pbG+rv84Nvv5jV2xr43ppdbNpzhKK8LD60tIq7l88YtWwYL2a20d2rB3ss89/WAq/ub+NjD75IPGb87FPLztv/1DmVhXztTxfzmYc2875vr+Of777qtDnLC1Uy6bx2sJ01O5t4bsdhNta3pk7PN5hTUcis8gJWXlrB7qYOsmIx+pLO8Z4+2rv6aDzazYNr607MR0/Kz2bx9Eksnl7M4osnccX0SVRMzB3nHp6b7r4ELR09NB7tZndTB3VNndQ1d7C7qYPaw8dOjOLzsmMsnFbMR5bNoHpGCVfNKOGZ1/XdDMMVjxm3LprKrYumsrG+lR+s28331+7me2t2ccvCKXz82llUzyjJuMFFxo/0Wzt6+PrTO/jpC/WUFebys08tY05l4Si1MCUdlw14cXcLn3loE8e6+vhP75rDXUurLsgLUR1sO8662mbW7Gxk7c4mmjt6AFgwtYh3zqtg+SVlLKma9JZPNEP9PP+kejo7Dh1ly962YG72CDsOHT1x1EnFxFxmBaPdyqLUDsmC3Cxys2Jkx/tvRl/C6Ukk6e5L0NOXpKcvSXdfMrWuN/Vvb1+SvqTTl0ySSDq9CQ/+Td13h6Q7Tmouvf9+0jlt3VC1Tmoq60hnLy0dPSdCfaBpxXnMLC9gTmUhiy4qZm/rcSoukB2t57vB/j73HznOj9fX8X9f3Evb8V5mlOXzR4umctNlk1l0UfEFM914ppF+qNA3sweAy4Bfu/t9YWvCrhvKSEO/paOHZ147xOrXDrG2tpGeviR3L5vB526al5bgTNe1Yg63d/GFR7by7BuN5GXHuHXhVJbOKmVJ1SSqSvPPm/nH3kSSxqPdNLR3sae5k12Nx9jecJSt+9poaO8CUifZXDe3nOvmlrNiTjmVZ7iE9XB+np09fWw70M6WvUd4o+Eodc0d1Dd30tzRQyI5sgFN3Ix4zIjFINa/fOLf1DozMFL/AqffJ3VmqQWPQf9z3lprBhOy4xTkZqVuOVkU5mZRVphDaUGOrr6aRmf6++zs6eNXWw7wxNaDPP9mM4mkU5AT56qZpSyYOpFLJ0/kokkTKA+OdirKyzqvPhGc0/SOmd0BxN19uZn9wMzmuvvOs9UAi8KsO3Vbo2HNzka+8MhWphXncefbq/jQ0iounXLhTZFUFuXxw48vZXtDOz9cV8fq1w6duCIoQPGEbCblZ5ObFSMvO05eVpzc7Nhpo8CB9wb+Yp76KzrwdzbpqVFoavSaGuUmk5Bw53hPgo6ePjq6E3R093G8N/GW7cQMZpYVsGx2KVdMn8TyS8qYP2XiOf9RnOlY8PycLJZUlbCkqiRov9PZk6C3LzUy73MnkXASySSxmJEVj5EVM7JiqTDPisXIip8MeMl8Z/p9uuvqKj749tStpaOH599sYsOuZmrqWln/ZhO9ibcOKHLiMcoKc8jLjpMdN7LjMbLiMXIGLMcMPPgkCKlPfal/oSeRpHPA31RnT4KPXzuT/3LzpaPe77OO9M3sm8CT7v4bM7sTmODuD56tBlgSZt0g27oHuCe4eynwxrl3M+3KgabxbkQaZXr/IPP7mOn9g8zv43D6N8PdKwZ7IMz8QAHQP7xsAa4MWRN23Vu4+/3A/SHadd4ws5qhPkplgkzvH2R+HzO9f5D5fRyt/oWZMDxGapQOUDjEcwarCbtORETGSJjQ3QisCJYXA3Uha8KuExGRMRJmeucxYI2ZTQNuBe40s/vcfdUZapaR2l8RZl0muKCmo0Yg0/sHmd/HTO8fZH4fR6V/YQ/ZLAFuAv7g7g1ha8KuExGRsXFen5wlIiKjSztSRUQiRKF/jszsATNbb2arzl59/jKzYjP7rZmtNrNHzSxnsL5lQn/NbLKZbQ6WM66PZvYdM3tPsJxR/TOzEjP7jZnVmNm/BOsyoo/B7+WaAfdD9Wu4fVXon4OBZyIDs4Ozji9UHwa+7u43Aw3AnZzStwzq7/8GJgzWnwu9j2Z2HTDF3X+Vif0D7gZ+GhyvPtHMvkAG9DHY1/kjUucyDZoto/X/qdA/NyuBh4Pl1Zw8HPWC4+7fcfeng7sVwEc4vW8rB1l3QTGzG4AOUm9sK8mgPppZNvA9oM7M3kuG9S/QDCw0s0nAxcAsMqOPCeCDQHtwfyXh+jXYujNS6J+bU88wnjyObRkVZrYcKAH2cnrfLuj+mlkO8N+BLwarBuvPhdzHjwKvAV8FlgKfIbP6B7AWmAF8FngdyCED+uju7e7eNmBV2N/NYfdVoX9uMuoMYzMrBb4FfILMPKP6i8B33P1IcD/T+rgEuD84FPpfgT+QWf0D+BvgL9z9K8B24C4yr4+QxqscXIg/jPNJxpxhHIyCfw58yd3rycwzqm8EPmNmzwJvA95DZvWxFpgdLFcDM8ms/kHqU+giM4sDVwP/i8zrI6TxKgfnxwXZL1yDnYl8ofokqQvg3Wtm9wIPAndn0hnV7v6O/uUg+G8ns84afwD4QXAF22xS872PZ1D/AP6e1O/mDGA98H/IrP/Dfmm7yoFOzjpHmXyGcRTOqM70PmZ6/yBz+5iuqxwo9EVEIkRz+iIiEaLQFxGJEIW+RI6Z3RbMg2JmVWa2dITbWWwpseDEqIGP5ZiZ/r7kvKM5fYkUMzNgE6nju3OBYuDPgG8GJQ3AbcB/BQ4AFwFJ4CCpwwXXuPt/DrZ1H6kzKR8FvhYsLwVeJHVk3H9099cGvPbHgH8jdYidu/tTaeyqyKAU+hIpZvZuUodqriZ12F+S1GFvBsSBDaSOdc9x9++b2V8AXe7+QzNbCfyRu38+2FaM1MlC/wOocPfDZrbB3ZeZ2SJgu7v3BrUzgF+QOn6+EngCuM7du8ao6yKAQl8ixszWAZuB+0idtdpJKvhjQJ+732FmdwOrSI3upwWPNwCTgCfd/Ytmlj0g0C8Cfuzu7xoQ+juAG9x9X1DzOPBP/aN7M/sr4B3Anf3bERkLOjlLIsPMPgrsDu4eIXU2Z98gpQ784xAj/VuCmr8zs1uAJ9z9S2bWZWbzg9e5Ftg0IPA/F7ze6hMv4P6N4M1inZn9ubtvHfUOiwxCoS9R8iSp09Y/Tep3fwqpefj+6Z3+N4Cz/l24++fN7JfATcElAb4A7Ase3g/8bbB+OnADsAN41czKSb3xxIPabxNcTldkLCj0JTKCOff84G4P8OenlOwidf2hAuDTweUMLgKSZvYRUjtynz7lOQ78JfC+YHkB8P3gsT8EFwbr/0KTjcAcd/9KMMf/dXf/0Wj2UeRsFPoSVX2k5vBv7F9hZr8OFi8D/oO7rzvD9E6/MlL7xt4VbGPDwG2e4o+BfwiWp5G6fLXImFLoS9T0/847sMTMnhnwWH5wSOcy4PPBOgtu/c9NDKhfCNwM3HG2FzWzz5J6k3khWDUPhb6MA4W+RE0hqS/eyAY2nzLSXw1cD7zk7p3B6nag18zmkfqaxb8asK0ngIfdveWU7Z8QvIn8gtQ3Pn0kWPc3pC6Q9dFR7JdIKDpkU+QUZpbj7j2juL0Cd+8Yre2JnAuFvohIhOjaICIiEaLQFxGJEIW+iEiEKPRFRCJEoS8iEiH/H6tQcEWmOwGhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a2008730>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1de31f0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1ef4a00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RCIpdqA89UWpmGCq/iEg0xC/UU4PnqUNQV1f5RUSiIHahnh6Qp2vp6W2VX0QkCmIX6kOnNEIQ6hqpi0gUZBXqZnazma0ws6tH+H6lmT1kZo+Y2T1mVpjbbubO0NkvkC6/KNVFZPIbNdTN7AIgz91PB+ab2YJhml0KXOfu7wUagD/KbTdzZ+g89fS2RuoiEgX5WbRZDNwVbj8CnAG8mtnA3W/M+GstsHPoTsxsCbAEYM6cOQfR1dwYbqSe0EhdRCIim/JLGbAt3G4G6kZqaGanA9XuvnLo99x9qbsvcvdFtbW1B9XZXEiOMFLXiVIRiYJsQn0vUBJul4/0HDOrAW4APp6bruXWtx58mSfX7+pfpXHQSD2h8ouIREM2ob6aoOQCcDywcWiD8MTo3cA/uPumnPUuR5Ip56blG3hwzfZ91lOH4ESpRuoiEgXZhPq9wGVmdh3wEeBFM7tmSJtPACcBXzazZWZ2UY77+aa0dvaScmjp6Bmx/KKRuohEwagnSt291cwWA+cA17p7A/DckDbfB74/Jj3MgeaOnuBre0//CdHBoa4TpSISDdnMfsHdWxiYATPptLT3hF97h734yDRSF5GIiMUVpc1hqDd39IwwT133KBWRaIhFqLd0pEfqPfSNsEyATpSKSBTEItSb23sB6Es5ezqD7czZLzpRKiJREYtQT4/UAXbt7QYGl1+09ouIREUsQj1dUwdo2htsDy2/9CYV6iIy+cUi1Fvae/pvMD3cSH1uTSkbm9p5YdueieieiEjOxCLUmzt6mF1dCgyEeuZI/Zyj6zi0uoRf/H4rO9u6JqSPIiK5EItQb2nv4fDaMgAahym/5OcluPS0uRQkjNtXbqa7Lzkh/RQRebNiEerN7T3MqSklP2Hsatu3/AJQWVLA+449hMa93by6Y+9EdFNE5E2LfKj3JlO0dvVRU1ZEdVnhsOWXtLopxQBsae4Y1z6KiORK5EN9d0cwL72mrICa0kK6+4K1d/Ns31CvLg3uwrelRaEuIpNTJEO9qzdJV29QF0/PUa8uK6S6rKC/TWKYIy8pzKO4IMGW5s5x6aeISK5FMtSvvPMZPvezZ4CBOeo1pYXUlA3cD3u48ku6nUbqIjJZZbVK42RTv7GF9KVE6RUaq8sK+8srMHz5Jd1ONXURmawiN1LftbebpvYemtt72LW3u38t9ZqywSP1xAgj9erSQra2dGqBLxGZlCIX6q/saBvYbmjrH6lXlRZkPVLv7kvRGE593La7kxWvNY1hj0VEcidyoZ45x/yVHW00t/dSXpRPUX5eViP1mtLgZGq6rv6vD6/lL25ZpQuSRGRSmPSh3tWbZEfrwKX963a0UVlSQFVpAet27KWlo6d/1kt1FidK06P5rS3BDJinN7bQ3ZfihW2tY3UIIiI5M+lD/bpHX+Gc6x7vn8L46o42FtZVcERdRThS76EmDOqaLMovVem56s0dNOzpYtvuINxXb2oey8MQEcmJSR3q7s7DLzTQ2tXHyg1NuDvrGtpYUFfOEXXl/aGeHqGPNk8doDA/wbTyIrY0d1IfBnlhXoL6jS39bVa93syzW3aP3YGJiBykSRXqjW3dLH3itf77ib7W2M7mcPrhb9fuZGdbN61dfSycUcHCugrauvp4dWfbwEi9bPSROsDsmhK2tHRQv7GFkoI8zjt2Bqs3teDu9CZTfOa21Vx55zOaISMibzmTKtRXbmjinx9cy2Mv7wCCIAc4+pAp/GbdTtY1BDNfFkyvYEFdBQBdvan+kXpJQR5F+cEhj1RTB5hdXcqWlg5Wb2rh+NmVnHbYVJrae9jY1MHj6xppau9hU1MH9ZsGRu8rXmvqn2kjIjJRJlWon/f2GcyqKuFHy18H4Ddrd7KwroJLTpvDluZOHnphOwBH1JVzRBjqMDBCNzNqygoxC7ZHMrumhDd2d/HS9lYWza1h0bxqAOo3NvPL32+lpqyQ0sI8frl6KwDPb93NR29ayZU/f3bQfhr2dGk0LxJzz23Zzaf/q56mcDHBsTapQj0/L8Hl75rHqo3NPLl+F09vbOasI6dz1pHTAfjl6m1MKy9kankRNWWFTCsvAhg0P726tHC/pRcIRurJlJNMOSfPq+ZtteVUlhTw65d38uuXd/LBE2Zx3tsP4YHnt9PZk+SfH3wZM3jilUaWv9oIwC9Wb+Ud3/o1Ny57rX+/TXu7+Z9nt/WXj0RkctvR2jVouvPGXe1c+qOV/de2dPUmuequZ/nfF3dwzQMvj0ufJlWoA1x0ymwqivK56ufP0pdy3nPkdGZVlXDkjAp6kqlBI/SFM8qBYIXGtJqywhHnqKfNrgnukmQGJ82pJpEwTp5bzcMvNtCTTHHhybO48ORZtHX38aV71rByQzNfft9RHFpdwrceXMvahlauvncNxQUJ/v2Rdax4rYnGtm4uWrqSK+98li/+9/OkUk5HTx9f+Pmz/Ml3f8eGxmB+fV8yxX+t3MT/PLutf5Tv7jy7Zfege60CtHX1vvk3VCRmhv723JdM8fqu9kGPr21o5eEXGuhLBqu6dvT08Y37X+Lr979IR08fAA+t2c6Z1/6WD31/BY1t3bS09/DxnzzNk+ubWPJf9azf2caNv13PhsZ2Fi+s5Z5ntvH4K41jfnxZrf1iZjcDRwMPuPs1B9smFyqKC7j41NnctPx1KksKOGlOFQBnHTmdtQ1tg0J9wfQKnlzfNHikXlbIKJnOM5uDmS3TK4p44PmgpJMfPunIGRUcM7OSVMqZVVXCPc9s47BpZfz5O+dRW1HElXc+y4d/sILyogLuvuJ0PnHr03z+zmeoLClgW0snF550KHfVb6WrN8W6hjZe3dlGeVE+53/3Sf7m3IXcvXpL/5z4u+u3cslpc1j6xAae3bKb8qJ8PnXmfBbOqODHT77OqtebOWlOFZ86cz5Jd+6q38q6hlbOPWYG558wi4272nlwzXbauvp47zF1vOtt03huy24ef6WR0sJ83nPkdBbUlbPq9WZWb2phdnUJ73rbNCqKC6jf1Mwr4fTQUw6robMnybNbdtPY1s0xMyt5+6wp7GjtZs22PfQmUxwzcwrzppaxqamDtQ2tlBbmc9QhFUwrL2J9415eb2yntqKIhTMqKMhLsH7nXhr2dHJodSmH15bT2Zvk9V17ae3sY87UUuZOLaVpbw8bm9pJOcybWsr0imK27+lkc3MHJQV5zJ1aRkVxPtt2d/LG7k5qygqZU1NKwowtLR00t/dQN6WYWVUldPYm2dbSSUdPH4dUljCjspiWjh62hdcjzKwqYWpZITvbunljdyelhfnMrCqmpDCP7Xu62NnaTU1ZITOrinGH7Xu62N3Rw/QpxRxSWUx7dx/b93TR2ZPkkKpiaiuKaGnv5Y09nRgwq6qEypICdrZ1s31PF2WFecysKqGoIMEbu7vYtbeb6tJg/6lUcCXzns5e6qYUcUhlCXu7e9m2u4vu3iQzq0qorShi195utrV0kp9nzKoqpbKkgO17OnljdxcVxfkcWl1CYX6w6ujOti5qK4qYXV1KbzLF5uYO9nT2MquqhFnVJezu6GVTUzvdfSnmTi1jxpTgvX59VzuF+QkOm1ZGdWkhG3e1s7Gpg5qyAg6vLacgL8ErO9rYtjv4WS6YXk5HT5J1O1pp2tvD26aXc3htOQ2tXbz0Ris9fSmOnjmFOTWlvLqzjTVbWykryuO4Q6uYWl7Ic1t288K2VmZVl7BobjUJM1ZuaOKVHW0cM3MKpx42lV17u3n8lUYa9nTxjvk1nHJYDWu27uHRl3bQl3LOPqqO4w6t5LGXd/DQmgamVRTywRNmMau6hDue2szDLzRw4pwqLjt9Hl09Sb63bD2bmjo47tBKrviDw/m/13Zxx1ObSXnw//1TZ87nxmXrea2xHTN4/JVG3n/sIdzw2/UcNWMK63fu5UM/+D+mlRextaWT71x8At/81ctcdvMqGtu6+dMTZ/HtC4/lfd9ZzpfvWcMjV72b0sKxW3Zr1D2b2QVAnrufbmY/NrMF7v7qgbbJpcvfdRg/fnIj7z6ilvy84JeNPzxyOt9f9hoLZwyE+lGHBNvTKor6H6stL6K4IG+/+68sKSAvYcydWtb/WHp7/rQy7nhqMwBH1FWwbXcnf3fuQgryEnzguJnctHwDL73Ryg//7GQOm1bGjZeexAe/9yR7u/q45fJTOO2wGmZVl3D9r1+lqrSAn1x+KodPL+czt63mq/e9yLTyIr57yYm0dPTyLw+t5Xfrd3FIZTFf/cDRPLWhmf947BUgCIlPnnEY//tSA5+5/fcAzKws5vhDq7jz6S38dMWm/nZTSgoG/eo3s7KY9p4kv/z91v7HppYV0tLRw/W/Wd//WGlhHh09g6+kzUsYSZWPJEfMYOhpp8L8BD3hfQ/SqkoLuHv11kFtppYV8sCa7f2PHVJZTH6e8ZtwAoUZnDKvhq0tnfztL54HYEpxPhecNIunXm/m8+FKrsfOquRvz13Iz5/ewv+7/ffkJYyPnT6PE+dUce3D6/jru59jekURt33iNBIGV931LNf/Zj1/eOR0vnvJSaxtaOXjP3maTU0dfOfiEzj/hFnMm1rGRUtXUFGcz9V/fBRF+Xl8+8Lj+PAPVvCfj73Kl9531Fi8ncFxj3Yiz8yuBx529wfN7GKgxN1vOYg2S4Al4V8XAutydRBv0jRg10R3YpzoWKNJxxpNwx3rXHev3d+TsvkdoAzYFm43AycdTBt3XwoszeL1xpWZ1bv7oonux3jQsUaTjjWaDvZYszlRuhcoCbfLR3hONm1ERGSMZRO+q4Ezwu3jgY0H2UZERMZYNuWXe4HlZjYTOA+42Myucfer99PmHbnv6ph5y5WExpCONZp0rNF0UMc66olSADOrBs4BnnD3hoNtIyIiYyurUBcRkclBJzRFRCIkNqFuZjeb2Qozu/rNtJkMRjsOM6s0s4fM7BEzu8fMCodrNxlk+zMzszoze2a8+jUWDuBYbzSzD4xXv8ZCFv+Gq83sQTOrN7Mfjnf/cin8t7l8lDZZZ1MsQj3zildgvpktOJg2k0GWx3EpcJ27vxdoAP5oPPuYKwf4M/s3BqbdTjrZHquZnQnMcPf7x7WDOZTlsV4G3B7O464ws0k5dz08F3krwbU+I7U5oGyKRagDi4G7wu1HGJh+eaBtJoPFjHIc7n6juz8a/rUW2Dk+Xcu5xWTxMzOz9wDtBB9gk9ViRjlWMysAbgI2mtn549e1nFvM6D/XJuDtZlYFzAa2jE/Xci4JXATs7ybIizmAbIpLqA+94rXuINtMBlkfh5mdDlS7+8rx6NgYGPVYw9LSPwJfHMd+jYVsfq4fA14CrgVONbPPjVPfci2bY/0dMBf4PPBy2G7ScfdWd98zSrMDyqa4hHqcrorN6jjMrAa4Afj4OPVrLGRzrF8EbnT3yX5T2WyO9URgaTil+DbgrHHqW65lc6xfBa5w928Aa4HLx6lvE+GAsmmyBteBitNVsaMeRzh6vRv4B3ffNH5dy7lsfmZnA581s2XACWb2o/HpWs5lc6zrgfnh9iJgsv5ssznWauBYM8sDTgOiPDf7wLLJ3SP/B5gCPAdcR/Cr2vHANaO0qZzofo/hsX4GaAGWhX8umuh+j9WxDmm/bKL7PMY/1wqCD+sngBXArInu9xge66nAiwSj2EeB8onu95s85mXh16PfbDbF5uKjOF0VG5XjyIaONZridKzZOJD3IzahLiISB3GpqYuIxIJCXUQkQhTqcsDCGQeRZWZjduWpmU0b5ftXZL6+mf3VCFdAzzazhJlNNbOZZjbDzEa8KlHeekZbHuBgl0JQqMsBMbNy4PHwSr7Mxw81swYzW2ZmG83sneHaMnnhP96fjrC/M83sb7N43a+Y2SdH+N6R4dWUmNmHzeyj4Xa1mZ07wnP+yczeN8LL3W5mZ5nZ1Wb2azN7zMyeCL8uN7NDwn18wsxKzGyumR1hZp8ys++Z2dvMbOEI+37QzOYM05/C8BgSwIkZwf5nDJnCZmZTgPsI7vX7duCvCWeImFlFxv6mmNm54bFWmlmpmV05WS+pj5JslgfgIJdCyOYmGSKYWTHQ7e57zew/gA8BPwq/Vwj0AA8TzKmdBvSGj5UDdwBfGWHXqwn+8Y6mO9zfcN4NXE8QGZEAAAcTSURBVGtmHwYqM/p0C/CDjGO4BjiFYE7z24BzzezzQBFwvbvfEzb9MvBDd383QVBeCJzl7n855HUbCG5k8EtgJrCA4JL1swAjvLm6mT0JtIWvsxBYambpfRQSXNpfALwfSIXHU2Rm3wTmAI9a8IRfAT8leD8fC9vOIvhZVIX7+AZwFXA48FFgerh9FcH01Y8CL5rZGcAb7r5hhPdUxlZ6eYD/ATCzUoKf7XRgjbt/loNcCkGzXyQr4cU73QSBOBfoY+DS5UKCsPgXgkCvJwigrwFPAQ+5e33GvmYDvycMvSEWAWXunjSzlzNeYw5BqDcAxcBed+9fiMzMriAIrfRFGr1Al7v/fITj+Rqw0t0fHuH7Be7eG46YnyG4MvV37r4r/P4UoNfdO83sT4ArgKkEF8W8Cmx2988M2ecPgcfd/Y4RXvOq8D05Cnia4MNuKcFo7gzgWwR3FptB8AGyleCCo5PC9+xL4WhuPcGHyKMEQV8DbAe+QzDyvx94L/Cku0/qFQ4nOzNb5u6LzeyvgCp3/5qZ/TfB/509BD/ztcChwGfdvXe0fWqkLllx98Xp7fAf4G53/0nGY7XpzfDrV4DTCUbqf2hm+cC17n4fQTj/2t0vHvo6ZvaauyfDv/a6+9nh438DNLj7bWY2D/hu+PgUgisnVxOMWIfu74cE/yG6wuPoG+74wpF9H3AxsAS4w8xuAm4EOsNmd5rZ1919OTCP4LeDfyUYWT9DEJ6VwAag2MyOdfc14f7/FPhT4HAzSy/NUAn8n7tfGZZrLiC4AnY1wUVEm4DjCAJ6U/i+/MrMTgFmu/sN4X4eMrNvmtkDBL8xnB1+KH6H4IMYgg/eU4BvEwR9NfDgcO+FTIiFwDvNbDHBb12zgL8iWAqh1cy+QLAUwqi3uFOoS1bM7FKCsOsl+FWwz8z+jCAsbgAeD5sWhF+/DXwJ+Avgm0Bm6cIJgv53w7zUjIztVBZd6wWec/ezLViiNL02/G53fzh8jW6CEe5fmpkTBNqpQKeZPZHR70+7+x1m9gbwAYJfh18kuJoPgiWLHzSzj7j782b2foIPsZ3he5A2E/hFRqCfH74fG4H/zGh3NAOLM5USjKxPAj5NMKLeRVDieo3gN5+0BuBdZvbYkPeiErjM3ZvCvxcR/JoPUOruXzGzs4H3uvvfjfB+ysRYB6xy91vCf1ebGVgKYSXBUghDf97DUqhLVtz9duB2GHGkfmi42Utw6XZf+LxdZjYnY/QNwUjkQXf/86GvY2ZrzCzh7ikgPyO45gA9ZvYXBOWX9FKlmfXDrxCEIQT19HRpxT1YX/z+8DeGewhONFYCd7v7zcMcci9wq7s/Fh4v7r7DzC4hqHXi7n0WrIS4EtgRviYEo+vZ4fEsIvgw/DDBKKsr4zV60v1392fMbD1wJ8Gv4ZvD508B/gD4esbz8gnKTx80s3OADe7+WvhedYTP+yDwhbBtGbDHzNoJgmKfE7Uy4W4CbjGzywn+bV9CUHq5haDcuQL4WTY7UqhLrrxB8A/zCmCtu68ys/QJ1kfM7NLwgwGC1QTXjLCfU8NAB/i4u6+CfcovxcARwzy3lrAsA+wz7dKCmTu3Ab8ADiOoWy8JZyL8R8YHTz6Au6c/UAoyHluXsb9zCD6gnGCknZ6uWMlAWNcDfxyWjA5n8BLAlQz8hpN+nTKgw8w+5u4/JRitdw8pGxVkbH8NuNyCaabJjH7ea2bvBn4LnExQyrrfzH4GTDezOnffMfQ9kvGVLmu6ezvwkSHfXgUcc6D71JRGGZWZFYdn50f6fj7BYlLfJCgzXBkGSg1BSeFu4KNhCEIwar1vuH25e2fG9qqMbyUI6/Xu3uXuzw/z3Fnuvjj8j3JyGNZVgFuwdvyTBFPEbg2fkiT4jzQLeNnMzjCzBMGyrl8ws783s0rgesJZCuHxloXH/PcEJx8hmFL4yfDPH4/wVj3l7men/wB/Qxj+4Yj+0XCfHwR2mtl9BL+WbzCzH4cnmCGo2X/bzG4mOOFcThAA9xHOkLBgumYZQempF6gNa/n5BCP4W/f3M5XJSyN1yca5wOcypuEBENbUIRgV30Iw2n3RzD5CECQ/AH7l7s3htMBCC+6dWeDurxxgH8oZqJdnGvbfsLt7WIt8ITxp+AzwJz6w1HA5kO/u3cBVZvbvBDNEzif4reM44FMEd5qBYIphOcGUs38nCND7wpNYxcAd7v7F8H25Hnhhf/00s1OBmwmCHYLa/YXuvjH8wDgX+Cd3fyps/3GgJhzxX09QZ/9peOKZ8EN0CXB3OBvnCeB54F6CD4rlwAPAJe7eaGZ3EHx4DDsTRyYvTWmUcRWOfMvdfduojd/8a+WPNNtllOcVhWE/9PE8gs+LbE7gikwIhbqISISopi4iEiEKdRGRCFGoi4hEiEJdRCRCFOoiIhHy/wFpb+4AKyLQbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a25c42e0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a2695eb0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a2767250>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a2007940>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1e73700>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a20416d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1e28850>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1f73c40>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1ecfd30>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1d3e5e0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1d5d8e0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a240ed00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a22626d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a27c0490>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1da7340>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a21b9880>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1498160>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a210b490>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EWRFxRKvbotaZ9QEfEVdGxK0RsX7y0mWJiGURsXHE42f0RTv0T0QsiYgbImJDRPxjRMxp176Ap6ZGvwa8CvjniOhp5/6Apz4rt9fLbdMXszrgR15BC5xYX0HbFuoP8ReprjcYsy/aqH/eAVyamWcD24C1tG9fALwUeH9mfhz4J+BM2rs/AD4JzG+3z8msDnhgDXBNvbyB4dMy28EA8Fagr368hmf2xVjripOZl2XmN+uHPcA7adO+AMjM72Tm9yPitVSj+F+ijfsjIs4E9lB9+a+hjfpitgf86Ktll7WwLQdVZvZl5q4Rq8bqi7bqn4hYBSwFHsS+CKoBwA6qU5bbsj8iYg7wx8CF9aq2+pzM9oD3atlhbX01cUQcBnwaeDdt3hcAWXkv8H+AV9O+/XEhcFlm7qwft9V7Y7a/EK+WHda2VxPXo7SvAB/MzPtp474AiIgPRMS76oeHAp+gffvj9cB7I+Im4GXAG2ijvpjtf/BjrCto29Xz4WriVnkP1c3sLoqIi4AvAOe3aV9AdYXmNRFxAfCvVO+Nm9uxPzLztUPLdci/kTb6nMz6C528WnaYVxMPsy+ezv4Y1k59MesDXpI0ttk+By9JGocBL0mFMuDVchHR2eo2jBYRvx0R80c8/v2xrnCMiGMjoiMiDo+IFRGxPCIWzMD+l0fEa6a7HbU3A14tFRELge9ExKGj1h8TEdsi4qaI2BIRr67vNdNZ31fkqnG29wsR8V/Gee7MiNgYEd+KiJvrn0PLp9Zl5kREN9Vn47QRIf9ORp0+FxGLgeuo/p7wi4H/THWa3cURsWjE9hZHxC9FxMfr++YcEhG/FxErR2xrYUR8eMTm3wW8YpzX8dGIOKPe3oURsSgi/un5+EWp1prtp0lqloqIecC+zNwdEX8OvAW4on5uDrAf+AbVOcpHAAfqdQuBq4EPjbPpzcD5Yz2RmTcCN9b7+N7IU+hGWAv8CjAIvBaYGxEfA44DvllfIfo14Kq6Hd+qyx5dv4ZDgW7go8AfAD8DvA04sl7+A+Cmet1dEbEaeCgz76t/G7ggM68A3g7sre+TArAjM3+1/kLsA1bV21wOHA/sycyBiOioX+vgOP2jNmLAq1W+AeyLiKQKqP6IeEv93ByqAITqPiqbqM5VBvhd4AOZuWloQxFxLPC/gZ+MWHdLvbgSWJCZA6P23z9WozLzqog4vN7WzwE/AP4TcC7VJe2rgUupzpf+W+Ak4GzgHqrzzxdk5h9FxMr6t5L/V9fpBg6rlzcD91OF89nAd4HP1q/tsxHxALAR+BPgCeCFDF9qvwQ4HHgf8EPge/XyCyPi5rrsm4F/Gev1qb0Y8GqJzFwztBwRvw/szMy/HrGuZ2ix/vkhqlHrQuB1EdEFXJKZ11GN7L+dmWtH7yci7q1HtkuoLvjZVz/14oi4keo3g6y3+zbgEOA8qisgN1NdIXs/1R0aHwfur78svhYRrwSOzcxP19u8ISI+FhFfB1YAr6/3/Zcj9jsHeCXV1aXdVPfOub7ukz3AOyPiz4CPAOuobpD1GPCluv4AcALw36mmhpbXbbuI6kvmtzLTcBdgwKtFIuIdVAF2ADiWagT/TqoA/DTwnbpod/3zE8AfAb8JfIxq1DokqUL/Fp5pOUB9Y7Yz6n2vAP4c6AU+mZlbRrTrNOCbVFfG/hbV3PujVNNH91JNzwzZBrwmIr41ap9LgPMz87H68VyqYAY4JDM/FBGvB87OzP86ql+uAN6XmXsj4kiqqZmvjijSRRX+51P9JnEA+FOq+fr9wH1j9IHalAGvlsjML1GPSscZwR9TLx6guhlUf13v0Yg4btSUy6HA9Zn5G6P3ExF3RkTHqDnpXwW+TTXyHd2u2yPiHuDvgEMz84F6O4uBX6QK1yFdwO7MfHNEnAXcl5n31oH/RF3vzcD767ILgF0RsYdq5H7cqLa+rm7D3nqu/9V1mc+OKHY8cDHV1NCpVPdXeQHwD/XzN4x+TWpfnkWj56uHgM9R3b71x0PTDvXB2Q31bwBDTgPuHGc7rxoZ7vXo/QKqAGfE+mNGnIXSTRXGT8TwTbseBXozc+TcffeI5Q8DnfU2BqgHT5l5LdU8+UeALwNfrUfk5wBHRsSyev8LgI8DH6y3dyHVQdxbIuKpL5XM/C7Vvcu/DfwN8KXMPEB1DOLNVMcMJMCAVwtExLyIOGSC57uARVRTMZ8Afi+qP15xGNX0yVeAt9WjZoBfpzpd8Rky88kR2z2Kaorlg5nZR3X2y9Bc/0eAs+tTF78JfIAqMB+JiOuoDrreFxGfrw/qQjUd8omIuJIqYBdSHdy8juqe9ETEL1N9Weyj+m2kJyLeTfUF8H7gi3Vf/DLVQduuiPgisCIzP5WZfwEcHtWfIjyx3u9fUQX8eqpjCS8ATqn38fLx+lXtx3vR6KCLiDcBvzNBkU6qO0LuyMyvRnUjqANUBz+/lpnbI2Iu1Xz9GuCCzHzTJPs8jioUL8zMv6/XnUl1pkon1W8Kb62LL8vMLfUXzX8D/i4zb6vrvJvq4Oti4FNU8/JX1Qd7h0bi64DXUd258BCqKaRrqb40NgJfB96emb31bwj9mXl1Xf9XgIWZOfo3jLcBPwIeAD5D9eXyp8CLgEuAPwQeBv5nve17J+oPtQcDXrNafXbMwszc2qDs4nrkPqtFRNfQVFE9V98xdEwiIiL9UKtmwEtSoZyDl6RCGfCSVCgDXpIKZcBLUqEMeEkq1P8HfDn9Wj2SUn0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lsw\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1ca2fd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1dc9580>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1d139d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a1fe7850>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9nu76FWdv7JunOW1vuv9VLaP7Pd6jqt7S/y/tVcAZVXXbhnZgSGsc7qSbC37VjMf/B5gadWwNPCrJy4D/pRvBLgVuBJ5fVXcDJPl1ugv2vA44uapekuSc/vY10/Z9PfDkJBfOeM0dgaOr6qfTXncqZLepqj9JcjBwaFW9etrzFgCTfShPtxswNV3zROBDwAvolgKf2z92FfAM4Afre4O0xfo2cHlVfTjJEcD3+8f/A9iLbmptgwxpjcvLq+rL69qQ5Pl0B/ZupDvo8uyquifJPXRTB3dPa34s3VwzwNP6g42TwAf7kfTh/bZFwG1VdWSSQ4Crq+p7fWj/vH/dI4GT+rbbArckuZ1uBL3XjDLv6F9nW+B5wMer6uYkD+XeefNrgOfSTaNcB5xD98diJ7pLdu42/NulLcQHgA8nOZbu8qZTU16voruG/M8H7cA5aW1WSV5Id0DuOcANVfV/G2i7M90Ieyqkv1BVR9JdDvOl0wIauumKKW8CFvYHKNfSD06q6jzgX+kO9v0d3Rz3BXRz5A9OMv0Sk+fQzVmfBHwPeFf/+DO491uiF9OdkfIzumtWnAf8N93V766mu0yp5oH+mARVdXtVvbCqDqqqI6rq1v7xN1bVR4fZlyGtcSi60el99KfZnUo3BZFpm7ZJ8h7gkX27Ryb5PeAgum/5uKdvf3iSL/ePn5vkK0lO6PdxNfC2JKfTHUnfDric7rS86/r9PpNuZHwH3dkiE0mOowvxk4Az+lPydu/b3AzcXVUXAScmWUoX+j9Osn9VfYsuwJ8J7E83zXMBXUjfBeyfZM5/C43Gy2t3aOSSvAB4B/f/xpkd6M6YeB7daPryqjqxD97X0c3bXUE3R/3mqvr7afvcB/ijmQfy+m0HAu+hC8wzq+r8/vFt6Y62P51u5L4N3VTEeXQH+S6hO7h5VFWtSXIM3Vzzl+jmwX/Yt5k6JW93uvNdfzEiSnfJ1ZOBI+hG0/sBv1NVP0jyp8BHq+qqjXoDNa8Z0mpCf2rSlf1c9KIZ89BzVpLFVXXn5q5Dc5chLUkNc05akhpmSEtSwwxpSWqYIS1JDTOkJalh/w9C5OFL2Rm8awAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a2543520>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9a26c91c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "OverflowError",
     "evalue": "cannot convert float infinity to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-07bef85fd735>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mI\u001b[0m \u001b[1;32min\u001b[0m \u001b[0muser_info\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muser_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mI\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mdistplot\u001b[1;34m(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)\u001b[0m\n\u001b[0;32m    213\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhist\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mbins\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 215\u001b[1;33m             \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_freedman_diaconis_bins\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    216\u001b[0m         \u001b[0mhist_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"alpha\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    217\u001b[0m         \u001b[0mhist_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"density\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm_hist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_freedman_diaconis_bins\u001b[1;34m(a)\u001b[0m\n\u001b[0;32m     33\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mceil\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOverflowError\u001b[0m: cannot convert float infinity to integer"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimHei'] #这个解决了中文无法显示的问题啊，真好啊。\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "for I in user_info.columns:\n",
    "    sns.distplot(user_info[I]) \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 异常值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199717, 34)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#研究数据发现需要删除的无关的列有以下几列。\n",
    "info_del=['客户编号','品牌','骑车销售商','货款日期','对接员工编号','主账户无效贷款次数','次账户无效贷款次数','贷款与已还贷款比列','主账户中已发放贷款','次账户中已发放贷款','贷款与已批准贷款比列','次账户没用还款','无效贷款总次数','已发放贷款总额','每月还款总额']\n",
    "user_info_01 = user_info.copy()\n",
    "user_info_01 = user_info_01.drop(columns = info_del)\n",
    "user_info_01.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#所有小于0的数，都按照0去处理，\n",
    "user_info_01[user_info_01< 0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#主账户贷款次数、主账户有效贷款次数、次账户贷款次数、次账户有效贷款次数，近六个月新贷款次数，贷款查询次数、这几个变量的值大于20都按照20去计算\n",
    "user_info_01['主账户贷款次数'] = user_info_01['主账户贷款次数'].apply(lambda x:20 if x >20 else x)\n",
    "user_info_01['主账户有效贷款次数'] = user_info_01['主账户有效贷款次数'].apply(lambda x:20 if x >20 else x)\n",
    "user_info_01['次账户贷款次数'] = user_info_01['次账户贷款次数'].apply(lambda x:20 if x >20 else x)\n",
    "user_info_01['次账户有效贷款次数'] = user_info_01['次账户有效贷款次数'].apply(lambda x:20 if x >20 else x)\n",
    "user_info_01['近六个月新贷款次数'] = user_info_01['近六个月新贷款次数'].apply(lambda x:20 if x >20 else x)\n",
    "user_info_01['贷款查询次数'] = user_info_01['贷款查询次数'].apply(lambda x:20 if x >20 else x)\n",
    "\n",
    "#主账户还款期数、次账户还款期数，大于50的按照50处理\n",
    "user_info_01['主账户还款期数'] = user_info_01['主账户还款期数'].apply(lambda x:50 if x >50 else x)\n",
    "user_info_01['次账户还款期数'] = user_info_01['次账户还款期数'].apply(lambda x:50 if x >50 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>50%</th>\n",
       "      <th>90%</th>\n",
       "      <th>95%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>已发货款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>54256.272280</td>\n",
       "      <td>1.297766e+04</td>\n",
       "      <td>13320.000000</td>\n",
       "      <td>53703.000000</td>\n",
       "      <td>68877.000000</td>\n",
       "      <td>7.402300e+04</td>\n",
       "      <td>9.529900e+04</td>\n",
       "      <td>9.905720e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产成本</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>75823.911139</td>\n",
       "      <td>1.892894e+04</td>\n",
       "      <td>37000.000000</td>\n",
       "      <td>70922.000000</td>\n",
       "      <td>99065.200000</td>\n",
       "      <td>1.095594e+05</td>\n",
       "      <td>1.554798e+05</td>\n",
       "      <td>1.628992e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与资产比列</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>74.643960</td>\n",
       "      <td>1.149048e+01</td>\n",
       "      <td>10.030000</td>\n",
       "      <td>76.670000</td>\n",
       "      <td>87.910000</td>\n",
       "      <td>8.937000e+01</td>\n",
       "      <td>8.995000e+01</td>\n",
       "      <td>9.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>车厂</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>69.085766</td>\n",
       "      <td>2.212829e+01</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>1.560000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>出生日期</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1983.876921</td>\n",
       "      <td>9.805565e+00</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1.997000e+03</td>\n",
       "      <td>1.998000e+03</td>\n",
       "      <td>2.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地区</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>7.245222</td>\n",
       "      <td>4.481338e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>2.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否填写手机号</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>受否填写身份证</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否出具驾驶证</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.023348</td>\n",
       "      <td>1.510067e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否填写护照</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.002143</td>\n",
       "      <td>4.624338e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>信用评分</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>291.762544</td>\n",
       "      <td>3.393176e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>762.000000</td>\n",
       "      <td>8.250000e+02</td>\n",
       "      <td>8.360000e+02</td>\n",
       "      <td>8.900000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>2.307690</td>\n",
       "      <td>4.048168e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户有效贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.045835</td>\n",
       "      <td>1.897589e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中尚未还清有效贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>168843.548481</td>\n",
       "      <td>9.636295e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>310802.800000</td>\n",
       "      <td>8.189590e+05</td>\n",
       "      <td>2.949083e+06</td>\n",
       "      <td>9.652492e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户中已批准的贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>222432.313449</td>\n",
       "      <td>2.522528e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>456208.000000</td>\n",
       "      <td>1.047165e+06</td>\n",
       "      <td>3.500000e+06</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.058082</td>\n",
       "      <td>5.629042e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户有效贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.027584</td>\n",
       "      <td>3.055123e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户中尚未还清有效贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>5589.772483</td>\n",
       "      <td>1.686663e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.032508e+04</td>\n",
       "      <td>3.603285e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户中已批准贷款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>7490.970053</td>\n",
       "      <td>1.818362e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>2.688820e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户每月还款</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>13144.154408</td>\n",
       "      <td>1.524289e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11622.400000</td>\n",
       "      <td>2.667220e+04</td>\n",
       "      <td>2.560731e+05</td>\n",
       "      <td>2.564281e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>近六个月新贷款次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.384920</td>\n",
       "      <td>9.534476e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>近六个月违约次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.095956</td>\n",
       "      <td>3.809351e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均贷款期限</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>8.058107</td>\n",
       "      <td>1.386076e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>1.170000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>13.190875</td>\n",
       "      <td>2.115686e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>9.700000e+01</td>\n",
       "      <td>1.170000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款查询次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.203273</td>\n",
       "      <td>6.919491e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>是否违约</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.177391</td>\n",
       "      <td>3.820002e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款与资产比</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.723575</td>\n",
       "      <td>1.136129e-01</td>\n",
       "      <td>0.094638</td>\n",
       "      <td>0.741715</td>\n",
       "      <td>0.853062</td>\n",
       "      <td>8.702865e-01</td>\n",
       "      <td>8.836521e-01</td>\n",
       "      <td>9.379874e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贷款总次数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>2.523561</td>\n",
       "      <td>5.356066e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>4.530000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>174430.089412</td>\n",
       "      <td>9.811908e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>325535.400000</td>\n",
       "      <td>8.509966e+05</td>\n",
       "      <td>3.021138e+06</td>\n",
       "      <td>9.652492e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已批准贷款总额</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>229923.283501</td>\n",
       "      <td>2.530977e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>480000.000000</td>\n",
       "      <td>1.088848e+06</td>\n",
       "      <td>3.580891e+06</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>主账户还款期数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>13.354897</td>\n",
       "      <td>2.042560e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>次账户还款期数</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.636936</td>\n",
       "      <td>5.440257e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>5.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>1.438913</td>\n",
       "      <td>7.922133e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工作类型</th>\n",
       "      <td>199717.0</td>\n",
       "      <td>0.487475</td>\n",
       "      <td>5.619145e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count           mean           std           min  \\\n",
       "已发货款            199717.0   54256.272280  1.297766e+04  13320.000000   \n",
       "资产成本            199717.0   75823.911139  1.892894e+04  37000.000000   \n",
       "贷款与资产比列         199717.0      74.643960  1.149048e+01     10.030000   \n",
       "车厂              199717.0      69.085766  2.212829e+01     45.000000   \n",
       "出生日期            199717.0    1983.876921  9.805565e+00   1949.000000   \n",
       "地区              199717.0       7.245222  4.481338e+00      1.000000   \n",
       "是否填写手机号         199717.0       1.000000  0.000000e+00      1.000000   \n",
       "受否填写身份证         199717.0       1.000000  0.000000e+00      1.000000   \n",
       "是否出具驾驶证         199717.0       0.023348  1.510067e-01      0.000000   \n",
       "是否填写护照          199717.0       0.002143  4.624338e-02      0.000000   \n",
       "信用评分            199717.0     291.762544  3.393176e+02      0.000000   \n",
       "主账户贷款次数         199717.0       2.307690  4.048168e+00      0.000000   \n",
       "主账户有效贷款次数       199717.0       1.045835  1.897589e+00      0.000000   \n",
       "主账户中尚未还清有效贷款    199717.0  168843.548481  9.636295e+05      0.000000   \n",
       "主账户中已批准的贷款      199717.0  222432.313449  2.522528e+06      0.000000   \n",
       "次账户贷款次数         199717.0       0.058082  5.629042e-01      0.000000   \n",
       "次账户有效贷款次数       199717.0       0.027584  3.055123e-01      0.000000   \n",
       "次账户中尚未还清有效贷款    199717.0    5589.772483  1.686663e+05      0.000000   \n",
       "次账户中已批准贷款       199717.0    7490.970053  1.818362e+05      0.000000   \n",
       "主账户每月还款         199717.0   13144.154408  1.524289e+05      0.000000   \n",
       "近六个月新贷款次数       199717.0       0.384920  9.534476e-01      0.000000   \n",
       "近六个月违约次数        199717.0       0.095956  3.809351e-01      0.000000   \n",
       "平均贷款期限          199717.0       8.058107  1.386076e+01      0.000000   \n",
       "第一次贷款距今时间       199717.0      13.190875  2.115686e+01      0.000000   \n",
       "贷款查询次数          199717.0       0.203273  6.919491e-01      0.000000   \n",
       "是否违约            199717.0       0.177391  3.820002e-01      0.000000   \n",
       "贷款与资产比          199717.0       0.723575  1.136129e-01      0.094638   \n",
       "贷款总次数           199717.0       2.523561  5.356066e+00      0.000000   \n",
       "尚未还清有效贷款总额      199717.0  174430.089412  9.811908e+05      0.000000   \n",
       "已批准贷款总额         199717.0  229923.283501  2.530977e+06      0.000000   \n",
       "主账户还款期数         199717.0      13.354897  2.042560e+01      0.000000   \n",
       "次账户还款期数         199717.0       0.636936  5.440257e+00      0.000000   \n",
       "总贷款次数与总有效贷款次数比  199717.0       1.438913  7.922133e-01      1.000000   \n",
       "工作类型            199717.0       0.487475  5.619145e-01      0.000000   \n",
       "\n",
       "                         50%            90%           95%           99%  \\\n",
       "已发货款            53703.000000   68877.000000  7.402300e+04  9.529900e+04   \n",
       "资产成本            70922.000000   99065.200000  1.095594e+05  1.554798e+05   \n",
       "贷款与资产比列            76.670000      87.910000  8.937000e+01  8.995000e+01   \n",
       "车厂                 86.000000      86.000000  8.600000e+01  1.200000e+02   \n",
       "出生日期             1986.000000    1995.000000  1.997000e+03  1.998000e+03   \n",
       "地区                  6.000000      14.000000  1.600000e+01  1.800000e+01   \n",
       "是否填写手机号             1.000000       1.000000  1.000000e+00  1.000000e+00   \n",
       "受否填写身份证             1.000000       1.000000  1.000000e+00  1.000000e+00   \n",
       "是否出具驾驶证             0.000000       0.000000  0.000000e+00  1.000000e+00   \n",
       "是否填写护照              0.000000       0.000000  0.000000e+00  0.000000e+00   \n",
       "信用评分               14.000000     762.000000  8.250000e+02  8.360000e+02   \n",
       "主账户贷款次数             1.000000       7.000000  1.100000e+01  2.000000e+01   \n",
       "主账户有效贷款次数           0.000000       3.000000  5.000000e+00  9.000000e+00   \n",
       "主账户中尚未还清有效贷款        0.000000  310802.800000  8.189590e+05  2.949083e+06   \n",
       "主账户中已批准的贷款          0.000000  456208.000000  1.047165e+06  3.500000e+06   \n",
       "次账户贷款次数             0.000000       0.000000  0.000000e+00  2.000000e+00   \n",
       "次账户有效贷款次数           0.000000       0.000000  0.000000e+00  1.000000e+00   \n",
       "次账户中尚未还清有效贷款        0.000000       0.000000  0.000000e+00  2.032508e+04   \n",
       "次账户中已批准贷款           0.000000       0.000000  0.000000e+00  5.000000e+04   \n",
       "主账户每月还款             0.000000   11622.400000  2.667220e+04  2.560731e+05   \n",
       "近六个月新贷款次数           0.000000       1.000000  2.000000e+00  4.000000e+00   \n",
       "近六个月违约次数            0.000000       0.000000  1.000000e+00  2.000000e+00   \n",
       "平均贷款期限              0.000000      25.000000  3.700000e+01  6.400000e+01   \n",
       "第一次贷款距今时间           0.000000      41.000000  6.100000e+01  9.700000e+01   \n",
       "贷款查询次数              0.000000       1.000000  1.000000e+00  3.000000e+00   \n",
       "是否违约                0.000000       1.000000  1.000000e+00  1.000000e+00   \n",
       "贷款与资产比              0.741715       0.853062  8.702865e-01  8.836521e-01   \n",
       "贷款总次数               1.000000       7.000000  1.200000e+01  2.400000e+01   \n",
       "尚未还清有效贷款总额          0.000000  325535.400000  8.509966e+05  3.021138e+06   \n",
       "已批准贷款总额             0.000000  480000.000000  1.088848e+06  3.580891e+06   \n",
       "主账户还款期数             0.000000      50.000000  5.000000e+01  5.000000e+01   \n",
       "次账户还款期数             0.000000       0.000000  0.000000e+00  5.000000e+01   \n",
       "总贷款次数与总有效贷款次数比      1.000000       2.000000  3.000000e+00  5.000000e+00   \n",
       "工作类型                0.000000       1.000000  1.000000e+00  2.000000e+00   \n",
       "\n",
       "                         max  \n",
       "已发货款            9.905720e+05  \n",
       "资产成本            1.628992e+06  \n",
       "贷款与资产比列         9.500000e+01  \n",
       "车厂              1.560000e+02  \n",
       "出生日期            2.000000e+03  \n",
       "地区              2.200000e+01  \n",
       "是否填写手机号         1.000000e+00  \n",
       "受否填写身份证         1.000000e+00  \n",
       "是否出具驾驶证         1.000000e+00  \n",
       "是否填写护照          1.000000e+00  \n",
       "信用评分            8.900000e+02  \n",
       "主账户贷款次数         2.000000e+01  \n",
       "主账户有效贷款次数       2.000000e+01  \n",
       "主账户中尚未还清有效贷款    9.652492e+07  \n",
       "主账户中已批准的贷款      1.000000e+09  \n",
       "次账户贷款次数         2.000000e+01  \n",
       "次账户有效贷款次数       2.000000e+01  \n",
       "次账户中尚未还清有效贷款    3.603285e+07  \n",
       "次账户中已批准贷款       2.688820e+07  \n",
       "主账户每月还款         2.564281e+07  \n",
       "近六个月新贷款次数       2.000000e+01  \n",
       "近六个月违约次数        2.000000e+01  \n",
       "平均贷款期限          1.170000e+02  \n",
       "第一次贷款距今时间       1.170000e+02  \n",
       "贷款查询次数          2.000000e+01  \n",
       "是否违约            1.000000e+00  \n",
       "贷款与资产比          9.379874e-01  \n",
       "贷款总次数           4.530000e+02  \n",
       "尚未还清有效贷款总额      9.652492e+07  \n",
       "已批准贷款总额         1.000000e+09  \n",
       "主账户还款期数         5.000000e+01  \n",
       "次账户还款期数         5.000000e+01  \n",
       "总贷款次数与总有效贷款次数比  1.800000e+01  \n",
       "工作类型            2.000000e+00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info_01.describe(percentiles=[0.9,0.95,0.99]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#观察，对于太大的值进行标准化\n",
    "user_info_01['主账户中已批准的贷款'] = user_info_01['主账户中已批准的贷款'].apply(lambda x:3.500000e+06 if x >3.500000e+06 else x)\n",
    "user_info_01['次账户中已批准贷款'] = user_info_01['次账户中已批准贷款'].apply(lambda x:3.500000e+06 if x >5.000000e+04 else x)\n",
    "user_info_01['主账户每月还款'] = user_info_01['主账户每月还款'].apply(lambda x:2.560731e+05 if x >2.560731e+05 else x)\n",
    "user_info_01['贷款总次数'] = user_info_01['贷款总次数'].apply(lambda x:24 if x >24 else x)\n",
    "user_info_01['已批准贷款总额'] = user_info_01['贷款总次数'].apply(lambda x:3.580891e+06 if x >3.580891e+06 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199717, 34)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info_01.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>已发货款</th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比列</th>\n",
       "      <th>车厂</th>\n",
       "      <th>出生日期</th>\n",
       "      <th>地区</th>\n",
       "      <th>是否填写手机号</th>\n",
       "      <th>受否填写身份证</th>\n",
       "      <th>是否出具驾驶证</th>\n",
       "      <th>...</th>\n",
       "      <th>贷款查询次数</th>\n",
       "      <th>是否违约</th>\n",
       "      <th>贷款与资产比</th>\n",
       "      <th>贷款总次数</th>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <th>已批准贷款总额</th>\n",
       "      <th>主账户还款期数</th>\n",
       "      <th>次账户还款期数</th>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <th>工作类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>65532</td>\n",
       "      <td>78990</td>\n",
       "      <td>84.38</td>\n",
       "      <td>45</td>\n",
       "      <td>1981</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.829624</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>58413</td>\n",
       "      <td>67960</td>\n",
       "      <td>89.02</td>\n",
       "      <td>86</td>\n",
       "      <td>1977</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.859520</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>63882</td>\n",
       "      <td>78934</td>\n",
       "      <td>83.61</td>\n",
       "      <td>86</td>\n",
       "      <td>1983</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.809309</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>110987</td>\n",
       "      <td>165925</td>\n",
       "      <td>69.91</td>\n",
       "      <td>67</td>\n",
       "      <td>1972</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.668899</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>83800</td>\n",
       "      <td>138143</td>\n",
       "      <td>63.70</td>\n",
       "      <td>67</td>\n",
       "      <td>1970</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.606618</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98698</th>\n",
       "      <td>199705</td>\n",
       "      <td>28529</td>\n",
       "      <td>73000</td>\n",
       "      <td>40.41</td>\n",
       "      <td>86</td>\n",
       "      <td>1981</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.390808</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98699</th>\n",
       "      <td>199708</td>\n",
       "      <td>60113</td>\n",
       "      <td>73691</td>\n",
       "      <td>84.00</td>\n",
       "      <td>86</td>\n",
       "      <td>1993</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.815744</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98700</th>\n",
       "      <td>199710</td>\n",
       "      <td>65489</td>\n",
       "      <td>113590</td>\n",
       "      <td>59.86</td>\n",
       "      <td>48</td>\n",
       "      <td>1995</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.576538</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98701</th>\n",
       "      <td>199713</td>\n",
       "      <td>52303</td>\n",
       "      <td>72677</td>\n",
       "      <td>72.93</td>\n",
       "      <td>86</td>\n",
       "      <td>1985</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.719664</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98702</th>\n",
       "      <td>199715</td>\n",
       "      <td>54509</td>\n",
       "      <td>71921</td>\n",
       "      <td>77.86</td>\n",
       "      <td>45</td>\n",
       "      <td>1983</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.757901</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98703 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index    已发货款    资产成本  贷款与资产比列  车厂  出生日期  地区  是否填写手机号  受否填写身份证  \\\n",
       "0           0   65532   78990    84.38  45  1981   8        1        1   \n",
       "1           2   58413   67960    89.02  86  1977   9        1        1   \n",
       "2           7   63882   78934    83.61  86  1983   8        1        1   \n",
       "3           9  110987  165925    69.91  67  1972   5        1        1   \n",
       "4          10   83800  138143    63.70  67  1970  16        1        1   \n",
       "...       ...     ...     ...      ...  ..   ...  ..      ...      ...   \n",
       "98698  199705   28529   73000    40.41  86  1981   4        1        1   \n",
       "98699  199708   60113   73691    84.00  86  1993   4        1        1   \n",
       "98700  199710   65489  113590    59.86  48  1995   9        1        1   \n",
       "98701  199713   52303   72677    72.93  86  1985   6        1        1   \n",
       "98702  199715   54509   71921    77.86  45  1983   4        1        1   \n",
       "\n",
       "       是否出具驾驶证  ...  贷款查询次数  是否违约    贷款与资产比  贷款总次数  尚未还清有效贷款总额  已批准贷款总额  \\\n",
       "0            0  ...       0     1  0.829624      0           0        0   \n",
       "1            0  ...       0     1  0.859520      0           0        0   \n",
       "2            0  ...       1     1  0.809309      0           0        0   \n",
       "3            0  ...       0     1  0.668899      0           0        0   \n",
       "4            0  ...       0     1  0.606618      0           0        0   \n",
       "...        ...  ...     ...   ...       ...    ...         ...      ...   \n",
       "98698        0  ...       0     0  0.390808      0           0        0   \n",
       "98699        0  ...       0     0  0.815744      0           0        0   \n",
       "98700        0  ...       0     0  0.576538      0           0        0   \n",
       "98701        0  ...       0     0  0.719664      0           0        0   \n",
       "98702        0  ...       0     0  0.757901      0           0        0   \n",
       "\n",
       "       主账户还款期数  次账户还款期数  总贷款次数与总有效贷款次数比  工作类型  \n",
       "0            0        0             1.0     0  \n",
       "1            0        0             1.0     1  \n",
       "2            0        0             1.0     1  \n",
       "3            0        0             1.0     0  \n",
       "4            0        0             1.0     0  \n",
       "...        ...      ...             ...   ...  \n",
       "98698        0        0             1.0     0  \n",
       "98699        0        0             1.0     1  \n",
       "98700        0        0             1.0     1  \n",
       "98701        0        0             1.0     0  \n",
       "98702        0        0             1.0     1  \n",
       "\n",
       "[98703 rows x 35 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#总贷款次数为零的客户自然是没有违约的客户，为了避免干扰将这部分数据删除\n",
    "cond = user_info_01['贷款总次数'] == 0\n",
    "user_info_01 = user_info_01[cond].reset_index()\n",
    "user_info_01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index             0\n",
       "已发货款              0\n",
       "资产成本              0\n",
       "贷款与资产比列           0\n",
       "车厂                0\n",
       "出生日期              0\n",
       "地区                0\n",
       "是否填写手机号           0\n",
       "受否填写身份证           0\n",
       "是否出具驾驶证           0\n",
       "是否填写护照            0\n",
       "信用评分              0\n",
       "主账户贷款次数           0\n",
       "主账户有效贷款次数         0\n",
       "主账户中尚未还清有效贷款      0\n",
       "主账户中已批准的贷款        0\n",
       "次账户贷款次数           0\n",
       "次账户有效贷款次数         0\n",
       "次账户中尚未还清有效贷款      0\n",
       "次账户中已批准贷款         0\n",
       "主账户每月还款           0\n",
       "近六个月新贷款次数         0\n",
       "近六个月违约次数          0\n",
       "平均贷款期限            0\n",
       "第一次贷款距今时间         0\n",
       "贷款查询次数            0\n",
       "是否违约              0\n",
       "贷款与资产比            0\n",
       "贷款总次数             0\n",
       "尚未还清有效贷款总额        0\n",
       "已批准贷款总额           0\n",
       "主账户还款期数           0\n",
       "次账户还款期数           0\n",
       "总贷款次数与总有效贷款次数比    0\n",
       "工作类型              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info_01.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从上面可以看出数据中没有缺失值，所以无需处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 衍生字段的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>车厂</th>\n",
       "      <th>出生日期</th>\n",
       "      <th>工作类型</th>\n",
       "      <th>地区</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>1981</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>86</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86</td>\n",
       "      <td>1983</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67</td>\n",
       "      <td>1972</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   车厂  出生日期  工作类型  地区\n",
       "0  45  1981     0   8\n",
       "1  86  1977     1   9\n",
       "2  86  1983     1   8\n",
       "3  67  1972     0   5\n",
       "4  67  1970     0  16"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#要进行哑变量转码的有以下几个字段\n",
    "info_ya = ['车厂','出生日期','工作类型','地区']\n",
    "user_info_01[info_ya].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    40\n",
       "1    44\n",
       "2    38\n",
       "3    49\n",
       "4    51\n",
       "Name: 出生日期, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#首先对出生日期进行转变\n",
    "user_info_01['出生日期'] = user_info_01['出生日期'].apply(lambda x:(2021-x)) \n",
    "user_info_01['出生日期'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def cutif(x):\n",
    "    if x < 18:\n",
    "        return 0\n",
    "    elif  18<=x<25:\n",
    "        return 1\n",
    "    elif 25<=x<30:\n",
    "        return 2\n",
    "    elif 30<=x<35:\n",
    "        return 3 \n",
    "    elif 35<=x<40:\n",
    "        return 4 \n",
    "    elif 40<=x<45:\n",
    "        return 5\n",
    "    elif 45<=x<50:\n",
    "        return 6 \n",
    "    elif 50<=x<55:\n",
    "        return 7\n",
    "    elif 55<=x<60:\n",
    "        return 8\n",
    "    elif 60<=x<65:\n",
    "        return 9\n",
    "    elif 65<=x<70:\n",
    "        return 9\n",
    "    elif 70<=x :\n",
    "        return 10\n",
    "\n",
    "user_info_01['出生日期'] = user_info_01['出生日期'].apply(cutif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    28741\n",
       "3    19605\n",
       "4    13748\n",
       "5    10195\n",
       "6     8326\n",
       "1     7840\n",
       "7     5488\n",
       "8     3160\n",
       "9     1600\n",
       "Name: 出生日期, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info_01['出生日期'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#进行转码前，首先对数字转换成字符串类型\n",
    "user_info_02 = user_info_01[info_ya].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>车厂_120</th>\n",
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       "      <th>地区_20</th>\n",
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       "      <th>地区_3</th>\n",
       "      <th>地区_4</th>\n",
       "      <th>地区_5</th>\n",
       "      <th>地区_6</th>\n",
       "      <th>地区_7</th>\n",
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       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <th>98698</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>98699</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98700</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>98701</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>98702</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98703 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       车厂_120  车厂_145  车厂_153  车厂_45  车厂_48  车厂_49  车厂_51  车厂_67  车厂_86  \\\n",
       "0           0       0       0      1      0      0      0      0      0   \n",
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       "3           0       0       0      0      0      0      0      1      0   \n",
       "4           0       0       0      0      0      0      0      1      0   \n",
       "...       ...     ...     ...    ...    ...    ...    ...    ...    ...   \n",
       "98698       0       0       0      0      0      0      0      0      1   \n",
       "98699       0       0       0      0      0      0      0      0      1   \n",
       "98700       0       0       0      0      1      0      0      0      0   \n",
       "98701       0       0       0      0      0      0      0      0      1   \n",
       "98702       0       0       0      1      0      0      0      0      0   \n",
       "\n",
       "       出生日期_1  ...  地区_20  地区_21  地区_22  地区_3  地区_4  地区_5  地区_6  地区_7  地区_8  \\\n",
       "0           0  ...      0      0      0     0     0     0     0     0     1   \n",
       "1           0  ...      0      0      0     0     0     0     0     0     0   \n",
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       "3           0  ...      0      0      0     0     0     1     0     0     0   \n",
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       "...       ...  ...    ...    ...    ...   ...   ...   ...   ...   ...   ...   \n",
       "98698       0  ...      0      0      0     0     1     0     0     0     0   \n",
       "98699       0  ...      0      0      0     0     1     0     0     0     0   \n",
       "98700       0  ...      0      0      0     0     0     0     0     0     0   \n",
       "98701       0  ...      0      0      0     0     0     0     1     0     0   \n",
       "98702       0  ...      0      0      0     0     1     0     0     0     0   \n",
       "\n",
       "       地区_9  \n",
       "0         0  \n",
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       "3         0  \n",
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       "...     ...  \n",
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       "\n",
       "[98703 rows x 43 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#进行转码\n",
    "user_info_03 = pd.get_dummies(user_info_02)\n",
    "user_info_03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>车厂_120</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>98703 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       车厂_120  车厂_145  车厂_153  车厂_45  车厂_48  车厂_49  车厂_51  车厂_67  车厂_86  \\\n",
       "0           0       0       0      1      0      0      0      0      0   \n",
       "1           0       0       0      0      0      0      0      0      1   \n",
       "2           0       0       0      0      0      0      0      0      1   \n",
       "3           0       0       0      0      0      0      0      1      0   \n",
       "4           0       0       0      0      0      0      0      1      0   \n",
       "...       ...     ...     ...    ...    ...    ...    ...    ...    ...   \n",
       "98698       0       0       0      0      0      0      0      0      1   \n",
       "98699       0       0       0      0      0      0      0      0      1   \n",
       "98700       0       0       0      0      1      0      0      0      0   \n",
       "98701       0       0       0      0      0      0      0      0      1   \n",
       "98702       0       0       0      1      0      0      0      0      0   \n",
       "\n",
       "       出生日期_1  ...  第一次贷款距今时间  贷款查询次数  是否违约    贷款与资产比  贷款总次数  尚未还清有效贷款总额  \\\n",
       "0           0  ...          0       0     1  0.829624      0           0   \n",
       "1           0  ...          0       0     1  0.859520      0           0   \n",
       "2           0  ...          0       1     1  0.809309      0           0   \n",
       "3           0  ...          0       0     1  0.668899      0           0   \n",
       "4           0  ...          0       0     1  0.606618      0           0   \n",
       "...       ...  ...        ...     ...   ...       ...    ...         ...   \n",
       "98698       0  ...          0       0     0  0.390808      0           0   \n",
       "98699       0  ...          0       0     0  0.815744      0           0   \n",
       "98700       0  ...          0       0     0  0.576538      0           0   \n",
       "98701       0  ...          0       0     0  0.719664      0           0   \n",
       "98702       0  ...          0       0     0  0.757901      0           0   \n",
       "\n",
       "       已批准贷款总额  主账户还款期数  次账户还款期数  总贷款次数与总有效贷款次数比  \n",
       "0            0        0        0             1.0  \n",
       "1            0        0        0             1.0  \n",
       "2            0        0        0             1.0  \n",
       "3            0        0        0             1.0  \n",
       "4            0        0        0             1.0  \n",
       "...        ...      ...      ...             ...  \n",
       "98698        0        0        0             1.0  \n",
       "98699        0        0        0             1.0  \n",
       "98700        0        0        0             1.0  \n",
       "98701        0        0        0             1.0  \n",
       "98702        0        0        0             1.0  \n",
       "\n",
       "[98703 rows x 74 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除原转码字段，然后将转码后的字段并入进去合成新的Dateframe\n",
    "user_info_04 = user_info_01.copy()\n",
    "user_info_04 = user_info_04.drop(columns = ['车厂','出生日期','工作类型','地区'])\n",
    "\n",
    "#将进行合并\n",
    "user_info_05 = pd.concat([user_info_03,user_info_04],axis=1)\n",
    "user_info_05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 失衡数据判断并处理 10分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    79989\n",
       "1    18714\n",
       "Name: 是否违约, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info_05['是否违约'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从数据中看出数据没有失衡，不需要进行上采样和下采样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多个备选模型比较20分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "# 模型处理模块\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# 标准化处理模块\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 常规模型\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# 集成学习和stacking模型\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "# 评价标准模块\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score,roc_auc_score,recall_score,precision_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info_05.isnull().sum()[user_info_05.isnull().sum()!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把结果和过程变量分开\n",
    "user_info_06 = user_info_05['是否违约']\n",
    "user_info_07 = user_info_05.drop(columns ='是否违约')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>车厂_120</th>\n",
       "      <th>车厂_145</th>\n",
       "      <th>车厂_153</th>\n",
       "      <th>车厂_45</th>\n",
       "      <th>车厂_48</th>\n",
       "      <th>车厂_49</th>\n",
       "      <th>车厂_51</th>\n",
       "      <th>车厂_67</th>\n",
       "      <th>车厂_86</th>\n",
       "      <th>出生日期_1</th>\n",
       "      <th>...</th>\n",
       "      <th>平均贷款期限</th>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <th>贷款查询次数</th>\n",
       "      <th>贷款与资产比</th>\n",
       "      <th>贷款总次数</th>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <th>已批准贷款总额</th>\n",
       "      <th>主账户还款期数</th>\n",
       "      <th>次账户还款期数</th>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.829624</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.859520</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.809309</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.668899</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.606618</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98698</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.390808</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98699</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.815744</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98700</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.576538</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98701</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.719664</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98702</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.757901</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98703 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       车厂_120  车厂_145  车厂_153  车厂_45  车厂_48  车厂_49  车厂_51  车厂_67  车厂_86  \\\n",
       "0           0       0       0      1      0      0      0      0      0   \n",
       "1           0       0       0      0      0      0      0      0      1   \n",
       "2           0       0       0      0      0      0      0      0      1   \n",
       "3           0       0       0      0      0      0      0      1      0   \n",
       "4           0       0       0      0      0      0      0      1      0   \n",
       "...       ...     ...     ...    ...    ...    ...    ...    ...    ...   \n",
       "98698       0       0       0      0      0      0      0      0      1   \n",
       "98699       0       0       0      0      0      0      0      0      1   \n",
       "98700       0       0       0      0      1      0      0      0      0   \n",
       "98701       0       0       0      0      0      0      0      0      1   \n",
       "98702       0       0       0      1      0      0      0      0      0   \n",
       "\n",
       "       出生日期_1  ...  平均贷款期限  第一次贷款距今时间  贷款查询次数    贷款与资产比  贷款总次数  尚未还清有效贷款总额  \\\n",
       "0           0  ...       0          0       0  0.829624      0           0   \n",
       "1           0  ...       0          0       0  0.859520      0           0   \n",
       "2           0  ...       0          0       1  0.809309      0           0   \n",
       "3           0  ...       0          0       0  0.668899      0           0   \n",
       "4           0  ...       0          0       0  0.606618      0           0   \n",
       "...       ...  ...     ...        ...     ...       ...    ...         ...   \n",
       "98698       0  ...       0          0       0  0.390808      0           0   \n",
       "98699       0  ...       0          0       0  0.815744      0           0   \n",
       "98700       0  ...       0          0       0  0.576538      0           0   \n",
       "98701       0  ...       0          0       0  0.719664      0           0   \n",
       "98702       0  ...       0          0       0  0.757901      0           0   \n",
       "\n",
       "       已批准贷款总额  主账户还款期数  次账户还款期数  总贷款次数与总有效贷款次数比  \n",
       "0            0        0        0             1.0  \n",
       "1            0        0        0             1.0  \n",
       "2            0        0        0             1.0  \n",
       "3            0        0        0             1.0  \n",
       "4            0        0        0             1.0  \n",
       "...        ...      ...      ...             ...  \n",
       "98698        0        0        0             1.0  \n",
       "98699        0        0        0             1.0  \n",
       "98700        0        0        0             1.0  \n",
       "98701        0        0        0             1.0  \n",
       "98702        0        0        0             1.0  \n",
       "\n",
       "[98703 rows x 73 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info_07"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(user_info_07,user_info_06,test_size=0.3,random_state=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(X_train, y_train, X_test, y_test,\n",
    "               model,model_name):\n",
    "    \n",
    "    print('训练{}'.format(model_name))\n",
    "    \n",
    "    clf=model\n",
    "    start = time.time()\n",
    "    clf.fit(X_train, y_train.values.ravel())\n",
    "    \n",
    "     #验证模型\n",
    "    print('训练准确率：{:.4f}'.format(clf.score(X_train, y_train)))\n",
    "    \n",
    "    \n",
    "    predict=clf.predict(X_test)\n",
    "    score = clf.score(X_test, y_test)\n",
    "    precision=precision_score(y_test,predict)\n",
    "    recall=recall_score(y_test,predict)\n",
    "    print('测试准确率：{:.4f}'.format(score))\n",
    "    print('测试精确率：{:.4f}'.format(precision))\n",
    "    print('测试召回率：{:.4f}'.format(recall))\n",
    "    \n",
    "    end = time.time()\n",
    "    duration = end - start\n",
    "    print('模型训练耗时：{:6f}s'.format(duration))\n",
    "    \n",
    "    \n",
    "    return clf, score,precision,recall, duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练LR\n",
      "训练准确率：0.9953\n",
      "测试准确率：0.9958\n",
      "测试精确率：0.9885\n",
      "测试召回率：0.9894\n",
      "模型训练耗时：0.606266s\n",
      "训练DT\n",
      "训练准确率：1.0000\n",
      "测试准确率：1.0000\n",
      "测试精确率：1.0000\n",
      "测试召回率：1.0000\n",
      "模型训练耗时：0.209388s\n",
      "训练AdaBoost\n",
      "训练准确率：1.0000\n",
      "测试准确率：1.0000\n",
      "测试精确率：1.0000\n",
      "测试召回率：1.0000\n",
      "模型训练耗时：0.258355s\n",
      "训练GBDT\n",
      "训练准确率：1.0000\n",
      "测试准确率：1.0000\n",
      "测试精确率：1.0000\n",
      "测试召回率：1.0000\n",
      "模型训练耗时：11.302405s\n",
      "训练RF\n",
      "训练准确率：1.0000\n",
      "测试准确率：1.0000\n",
      "测试精确率：1.0000\n",
      "测试召回率：1.0000\n",
      "模型训练耗时：7.043532s\n",
      "训练XGBoost\n",
      "[16:52:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "训练准确率：1.0000\n",
      "测试准确率：1.0000\n",
      "测试精确率：1.0000\n",
      "测试召回率：1.0000\n",
      "模型训练耗时：2.368070s\n"
     ]
    }
   ],
   "source": [
    "model_name_param_dict = {    'LR': (LogisticRegression(penalty =\"l2\")),\n",
    "                             'DT': (DecisionTreeClassifier(max_depth=10,min_samples_split=10)),\n",
    "                             'AdaBoost': (AdaBoostClassifier()),\n",
    "                             'GBDT': (GradientBoostingClassifier()),\n",
    "                             'RF': (RandomForestClassifier()),\n",
    "                             'XGBoost':(XGBClassifier())\n",
    "                         }\n",
    "\n",
    "result_df = pd.DataFrame(columns=['Accuracy (%)','precision(%)','recall(%)','Time (s)'],\n",
    "                             index=list(model_name_param_dict.keys()))\n",
    "\n",
    "for model_name, model in model_name_param_dict.items():\n",
    "    clf, acc,pre,recall, mean_duration = train_model(X_train, y_train,\n",
    "                                                        X_test, y_test,\n",
    "                                                        model,model_name)\n",
    "    result_df.loc[model_name, 'Accuracy (%)'] = acc\n",
    "    result_df.loc[model_name, 'precision(%)'] = pre\n",
    "    result_df.loc[model_name, 'recall(%)'] = recall\n",
    "    result_df.loc[model_name, 'Time (s)'] = mean_duration \n",
    "\n",
    "result_df.to_csv(os.path.join('model_comparison.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上的预测模型非常的完美"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 6,\n",
       " 'max_features': 60,\n",
       " 'min_samples_leaf': 30,\n",
       " 'min_samples_split': 40,\n",
       " 'n_estimators': 20}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'n_estimators': [20], 'max_features': [60],\"max_depth\":[6,8],\n",
    "             \"min_samples_split\": [40],\"min_samples_leaf\": [30]},\n",
    "#4 * 6 * 5 * 4 * 4 * 5 \n",
    "model = RandomForestClassifier()\n",
    "grid_search = GridSearchCV(model, param_grid, cv=5, scoring='roc_auc')\n",
    "result = grid_search.fit(X_train, y_train)\n",
    "result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre = result.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 1.0\n"
     ]
    }
   ],
   "source": [
    "#精准率和召回率    在提供的有限参数里，模型质量和默认一样都是100%\n",
    "print(precision_score(y_test,pre),recall_score(y_test,pre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.model']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#优质模型保存\n",
    "import joblib\n",
    "#保存模型\n",
    "joblib.dump(result,'model.model')\n",
    "\n",
    "#加载模型\n",
    "#clf=joblib.load('model.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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